Journal
ATMOSPHERIC ENVIRONMENT
Volume 133, Issue -, Pages 156-164Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2016.03.040
Keywords
Satellite remote sensing; Aerosol optical depth; PM2.5; Nested linear mixed model
Funding
- Chinese National Natural Science Foundation [71433007]
- National Science and Technology Support Program [2014BAC22B02]
- Jiangsu Provincial Science and Technology Support Program [SBE2014070928]
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Satellite remotely sensed aerosol optical depth (AOD) provides an effective way to fill the spatial and temporal gaps left by ground PM2.5 monitoring network. Previous studies have established robust advanced statistical models to estimate PM2.5 using AOD data in China. However, their coarse resolutions (-10 km or greater) of PM2.5 estimations are not enough to support the health effect studies at urban scales. In this study, 3 lcm AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 products were used to estimate the high resolution PM2.5 concentrations in Yangtze Delta Region of China. We proposed a nested linear mixed effects (LME) model' including nested month-, week, and day-specific random effects of PM2.5-AOD relationships. Validation results show that the LME model only with day-specific random effects (non-nested model) used in previous studies has poor performance in the days without PM2.5-AOD matchups (the R-2 of day-of-year-based cross validation (DOY-based CV) is 0.148). The results also show that our nested model cannot improve the performance of non nested model in the days with PM2.5-AOD matchups (sample-based CV R-2 = 0.671 for nested model vs. 0.661 for non -nested model), but can greatly improve the model performance beyond those days (DOY-based CV R-2 = 0.339 for nested model vs. 0.148 for non -nested model). To further improve the model performance, we applied the buffer models (i.e., models fitted from datasets which ground PM2.5 were matched with the average AOD values within certain radius buffer zones of gridded PM2.5 data) on the 3 km AOD data since the buffer models has more days with PM2.5-AOD matchups and can provide more day -specific relationships. The results of this study show that 3 km MODIS C6 AOD data can be used to estimate PM2.5 concentrations and can provide more detailed spatial information for urban scale studies. The application of our nested LME model can greatly improve the accuracy of 3 km PM2.5 predictions. (C) 2016 Elsevier Ltd. All rights reserved.
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